The focus of precision medicine is on decision support, often in the form of dynamic treatment regimes (DTRs), which are sequences of decision rules. At each decision point, the decision rules determine the next treatment based on the patient's characteristics, previous treatment and response, and current status and tests. DTR estimation with ordinal outcomes is rarely studied, and rarer still in the context of interference - where one patient's treatment may affect another's outcome. In this talk, I will introduce our proposed robust method — the dynamic weighted proportional odds model (dWPOM) for ordinal outcomes. Moreover, in the presence of household interference, considering the possible correlation between treatments in the same household, I will present the covariate balancing weights, which rely on the joint propensity score, and methods for estimating that score. Lastly, I will illustrate dWPOM in the analysis of the Population Assessment of Tobacco and Health data.